Vocal fold injury engenders a complex inflammation and wound healing response in laryngeal mucosal tissue, which commonly leads to scarring. Because individual variability in the formation and treatment for vocal fold scarring is considerable, the clinical decision making process for medical management is daunting. The work proposed in this R03 application exploits a systems biology approach that combines experimental and computational techniques to develop a computational model to assist in understanding the inflammatory and healing process associated with vocal fold scarring. Such models have provided valuable insights into the pathophysiology at the individual level and suggested pathways for inflammation treatment and healing optimization that are patient-specific. The overarching goal is to generate a clinical tool that will ultimately aid clinicians i prescribing personalized treatment for patients with vocal fold injury. The development of such a clinical tool requires the collection of tissue-specific biological data. We will extend our preliminary computational model's infrastructure, based on the new vocal fold data, to enhance its biological representation. The specific aims of this application are: (1) to count immune and repair cells including neutrophils, macrophages, endothelial cells and fibroblasts in surgically injured rat vocal folds using flow cytometry, up to 4 weeks post injury; (2) to identify the cell source of a damage-associated molecular pattern molecule (DAMP) and measure the levels of the DAMP and cytokines in vocal fold tissue and blood serum from rats following vocal fold surgery using immunohistochemistry and enzyme-linked immunosorbent assay respectively, up to 4 weeks post injury; (3) to implement our existing computational model on a high performance computing platform with an expanded infrastructure through the addition of biological data.